Towards quantifying the impact of non-uniform information access in collaborative information retrieval

The majority of research into Collaborative Information Retrieval (CIR) has assumed a uniformity of information access and visibility between collaborators. However in a number of real world scenarios, information access is not uniform between all collaborators in a team e.g. security, health etc. This can be referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, there has not yet been any systematic investigation of the effect of MLCIR on search outcomes. To address this shortcoming, in this paper, we present the results of a simulated evaluation conducted over 4 different non-uniform information access scenarios and 3 different collaborative search strategies. Results indicate that there is some tolerance to removing access to the collection and that there may not always be a negative impact on performance. We also highlight how different access scenarios and search strategies impact on search outcomes

HaIRST: Harvesting Institutional Resources in Scotland Testbed. Final Project Report

The HaIRST project conducted research into the design, implementation and deployment of a pilot service for UK-wide access of autonomously created institutional resources in Scotland, the aim being to investigate and advise on some of the technical, cultural, and organisational requirements associated with the deposit, disclosure, and discovery of institutional resources in the JISC Information Environment. The project involved a consortium of Scottish higher and further education institutions, with significant assistance from the Scottish Library and Information Council. The project investigated the use of technologies based on the Open Archives Initiative (OAI), including the implementation of OAI-compatible repositories for metadata which describe and link to institutional digital resources, the use of the OAI protocol for metadata harvesting (OAI-PMH) to automatically copy the metadata from multiple repositories to a central repository, and the creation of a service to search and identify resources described in the central repository. An important aim of the project was to identify issues of metadata interoperability arising from the requirements of individual institutional repositories and their impact on services based on the aggregation of metadata through harvesting. The project also sought to investigate issues in using these technologies for a wide range of resources including learning, teaching and administrative materials as well as the research and scholarly communication materials considered by many of the other projects in the JISC Focus on Access to Institutional Resources (FAIR) Programme, of which HaIRST was a part. The project tested and implemented a number of open source software packages supporting OAI, and was successful in creating a pilot service which provides effective information retrieval of a range of resources created by the project consortium institutions. The pilot service has been extended to cover research and scholarly communication materials produced by other Scottish universities, and administrative materials produced by a non-educational institution in Scotland. It is an effective testbed for further research and development in these areas. The project has worked extensively with a new OAI standard for 'static repositories' which offers a low-barrier, low-cost mechanism for participation in OAI-based consortia by smaller institutions with a low volume of resources. The project identified and successfully tested tools for transforming pre-existing metadata into a format compliant with OAI standards. The project identified and assessed OAI-related documentation in English from around the world, and has produced metadata for retrieving and accessing it. The project created a Web-based advisory service for institutions and consortia. The OAI Scotland Information Service (OAISIS) provides links to related standards, guidance and documentation, and discusses the findings of HaIRST relating to interoperability and the pilot harvesting service. The project found that open source packages relating to OAI can be installed and made to interoperate to create a viable method of sharing institutional resources within a consortium. HaIRST identified issues affecting the interoperability of shared metadata and suggested ways of resolving them to improve the effectiveness and efficiency of shared information retrieval environments based on OAI. The project demonstrated that application of OAI technologies to administrative materials is an effective way for institutions to meet obligations under Freedom of Information legislation

A recommender system for process discovery

Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft

Data Leak Detection As a Service: Challenges and Solutions

We describe a network-based data-leak detection (DLD) technique, the main feature of which is that the detection does not require the data owner to reveal the content of the sensitive data. Instead, only a small amount of specialized digests are needed. Our technique – referred to as the fuzzy fingerprint – can be used to detect accidental data leaks due to human errors or application flaws. The privacy-preserving feature of our algorithms minimizes the exposure of sensitive data and enables the data owner to safely delegate the detection to others.We describe how cloud providers can offer their customers data-leak detection as an add-on service with strong privacy guarantees. We perform extensive experimental evaluation on the privacy, efficiency, accuracy and noise tolerance of our techniques. Our evaluation results under various data-leak scenarios and setups show that our method can support accurate detection with very small number of false alarms, even when the presentation of the data has been transformed. It also indicates that the detection accuracy does not degrade when partial digests are used. We further provide a quantifiable method to measure the privacy guarantee offered by our fuzzy fingerprint framework

On the Measurement of Privacy as an Attacker's Estimation Error

A wide variety of privacy metrics have been proposed in the literature to evaluate the level of protection offered by privacy enhancing-technologies. Most of these metrics are specific to concrete systems and adversarial models, and are difficult to generalize or translate to other contexts. Furthermore, a better understanding of the relationships between the different privacy metrics is needed to enable more grounded and systematic approach to measuring privacy, as well as to assist systems designers in selecting the most appropriate metric for a given application. In this work we propose a theoretical framework for privacy-preserving systems, endowed with a general definition of privacy in terms of the estimation error incurred by an attacker who aims to disclose the private information that the system is designed to conceal. We show that our framework permits interpreting and comparing a number of well-known metrics under a common perspective. The arguments behind these interpretations are based on fundamental results related to the theories of information, probability and Bayes decision.Comment: This paper has 18 pages and 17 figure

Beyond Traditional Collaborative Search: Understanding the Effect of Awareness on Multi-Level Collaborative Information Retrieval

Although there has been a great deal of research into Collaborative Information Retrieval (CIR) and Collaborative Information Seeking (CIS), the majority has assumed that team members have the same level of unrestricted access to underlying information. However, observations from different domains (e.g. healthcare, business, etc.) have suggested that collaboration sometimes involves people with differing levels of access to underlying information. This type of scenario has been referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, no studies have been conducted to investigate the effect of awareness, an existing CIR/CIS concept, on MLCIR. To address this gap in current knowledge, we conducted two separate user studies using a total of 5 different collaborative search interfaces and 3 information access scenarios. A number of Information Retrieval (IR), CIS and CIR evaluation metrics, as well as questionnaires were used to compare the interfaces. Design interviews were also conducted after evaluations to obtain qualitative feedback from participants. Results suggested that query properties such as time spent on query, query popularity and query effectiveness could allow users to obtain information about team’s search performance and implicitly suggest better queries without disclosing sensitive data. Besides, having access to a history of intersecting viewed, relevant and bookmarked documents could provide similar positive effect as query properties. Also, it was found that being able to easily identify different team members and their actions is important for users in MLCIR. Based on our findings, we provide important design recommendations to help develop new CIR and MLCIR interfaces

Anonymous subject identification and privacy information management in video surveillance

The widespread deployment of surveillance cameras has raised serious privacy concerns, and many privacy-enhancing schemes have been recently proposed to automatically redact images of selected individuals in the surveillance video for protection. Of equal importance are the privacy and efficiency of techniques to first, identify those individuals for privacy protection and second, provide access to original surveillance video contents for security analysis. In this paper, we propose an anonymous subject identification and privacy data management system to be used in privacy-aware video surveillance. The anonymous subject identification system uses iris patterns to identify individuals for privacy protection. Anonymity of the iris-matching process is guaranteed through the use of a garbled-circuit (GC)-based iris matching protocol. A novel GC complexity reduction scheme is proposed by simplifying the iris masking process in the protocol. A user-centric privacy information management system is also proposed that allows subjects to anonymously access their privacy information via their iris patterns. The system is composed of two encrypted-domain protocols: The privacy information encryption protocol encrypts the original video records using the iris pattern acquired during the subject identification phase; the privacy information retrieval protocol allows the video records to be anonymously retrieved through a GC-based iris pattern matching process. Experimental results on a public iris biometric database demonstrate the validity of our framework